Accurate Dose Prediction in Radiotherapy Using a Distance-Aware Diffusion Model

Core Concepts

A distance-aware diffusion model, named DoseDiff, is proposed to accurately predict dose distribution in radiotherapy by effectively utilizing distance information between surrounding tissues and targets or organs-at-risk.

Abstract

The paper presents a novel distance-aware conditional diffusion model, named DoseDiff, for precise prediction of dose distribution in radiotherapy. The key highlights are:

DoseDiff defines dose prediction as a sequence of denoising steps, where the predicted dose distribution map is generated with the conditions of the computed tomography (CT) image and signed distance maps (SDMs). The SDMs provide the distance from each pixel in the image to the outline of the targets or organs-at-risk (OARs).

A multi-encoder and multi-scale fusion network (MMFNet) is proposed to enhance information fusion between the CT image and SDMs at the feature level, enabling effective extraction and integration of local and global features.

Extensive experiments on two in-house datasets (breast cancer and nasopharyngeal cancer) and a public dataset demonstrate that DoseDiff outperforms state-of-the-art dose prediction methods in terms of both quantitative performance and visual quality.

Ablation studies validate the contributions of the proposed components, including the PSDM (physical signed distance map), multi-scale fusion, and transformer-based fusion module. The results show that incorporating distance information and enhancing the fusion strategy can significantly improve the accuracy of dose distribution prediction.

Compared to previous methods, DoseDiff can better capture the characteristics of radiation paths in the predicted dose distribution maps, providing valuable information for medical physicists to optimize radiotherapy planning.

Stats

The in-plane pixel spacings of the CT images in the breast cancer dataset ranged from 0.77 to 0.97 mm, with an average of 0.95 mm, and the slice thicknesses were all 5.0 mm.
The in-plane pixel spacings of the CT images in the NPC dataset ranged from 0.75 to 0.97 mm, with an average of 0.95 mm, and the slice thicknesses were 3.0 mm.
The in-plane resolutions of all images were 512 × 512, and the number of slices ranged from 50 to 129 for the breast cancer dataset and 69 to 176 for the NPC dataset.
The intensity ranges for CT images and dose distribution maps were set to [-1000, 1500] HU and [0, 75] Gy, respectively, and both were uniformly and linearly normalized to [-1, 1] for training.

Quotes

"Treatment planning, which is a critical com-
ponent of the radiotherapy workflow, is typically carried
out by a medical physicist in a time-consuming trial-and-
error manner."
"Previous studies have proposed knowledge-
based or deep-learning-based methods for predicting dose
distribution maps to assist medical physicists in improv-
ing the efficiency of treatment planning. However, these
dose prediction methods usually fail to effectively utilize
distance information between surrounding tissues and tar-
gets or organs-at-risk (OARs)."
"Diffusion models have been shown to provide superior image
sampling quality and more stable training compared with
GANs."

Key Insights Distilled From

DoseDiff

by Yiwen Zhang,... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2306.16324.pdf

Deeper Inquiries

How can the proposed DoseDiff model be further extended to handle more complex radiotherapy scenarios, such as adaptive radiotherapy or multi-modality imaging data

The proposed DoseDiff model can be extended to handle more complex radiotherapy scenarios by incorporating adaptive radiotherapy techniques and multi-modality imaging data.

Adaptive Radiotherapy: DoseDiff can be enhanced to adapt to changes in patient anatomy or tumor response during the course of treatment. This can be achieved by integrating real-time imaging data, such as cone-beam CT or MRI scans acquired before each treatment session, to update the dose prediction model. By incorporating these data, DoseDiff can dynamically adjust the treatment plan to ensure optimal dose delivery while minimizing exposure to healthy tissue.

Multi-Modality Imaging Data: To handle multi-modality imaging data, DoseDiff can be modified to accept inputs from various imaging modalities, such as PET, MRI, and CT scans. By fusing information from different imaging sources, the model can provide a more comprehensive and accurate prediction of the dose distribution. Additionally, incorporating features extracted from different modalities can improve the model's ability to capture complex relationships and variations in the tumor and surrounding tissues.

Integration of Clinical Parameters: DoseDiff can also be extended to incorporate clinical parameters, such as patient demographics, treatment history, and genetic information, to personalize the dose prediction process. By considering a broader range of factors, the model can tailor the treatment plan to individual patient characteristics, leading to more effective and personalized radiotherapy outcomes.

What are the potential limitations of the distance-aware approach, and how can they be addressed to improve the robustness and generalizability of the model

The distance-aware approach in DoseDiff may have some limitations that could impact its robustness and generalizability. These limitations include:

Limited Spatial Resolution: The accuracy of distance maps may be affected by the spatial resolution of the imaging data. Lower resolution images may result in less precise distance information, leading to potential inaccuracies in dose prediction. To address this limitation, enhancing the resolution of the input data or implementing interpolation techniques can improve the quality of the distance maps.

Sensitivity to Noise: Distance maps can be sensitive to noise in the input data, which may introduce errors in the dose prediction process. Applying noise reduction techniques or incorporating denoising modules in the model architecture can help mitigate the impact of noise on the distance-aware approach.

Complexity of Tissue Interactions: The distance-aware approach assumes a simplified relationship between tissues based on their proximity. In reality, tissue interactions in radiotherapy are complex and may involve non-linear dependencies. To address this limitation, incorporating advanced modeling techniques, such as graph neural networks or attention mechanisms, can capture more intricate tissue interactions and improve the model's performance.

Given the promising results of DoseDiff, how can the insights from this work be leveraged to develop more advanced AI-powered tools for comprehensive radiotherapy planning and optimization

The insights from the successful implementation of DoseDiff can be leveraged to develop more advanced AI-powered tools for comprehensive radiotherapy planning and optimization in the following ways:

Personalized Treatment Planning: Building on the distance-aware approach of DoseDiff, advanced AI models can be developed to personalize treatment plans based on individual patient characteristics, tumor biology, and response to treatment. By integrating patient-specific data and adaptive algorithms, these tools can optimize treatment outcomes while minimizing side effects.

Real-time Treatment Monitoring: AI-powered tools can be designed to monitor treatment progress in real-time by analyzing imaging data and dose distribution maps during radiotherapy sessions. By providing continuous feedback to clinicians, these tools can enable on-the-fly adjustments to treatment plans, ensuring precise dose delivery and enhancing treatment efficacy.

Automated Quality Assurance: AI algorithms inspired by DoseDiff can be utilized for automated quality assurance in radiotherapy planning. By analyzing dose distribution maps and comparing them to predefined standards, these tools can detect errors or inconsistencies in treatment plans, leading to improved safety and accuracy in radiotherapy procedures.

Integration of Multi-Modal Data: Leveraging the multi-modality imaging capabilities of DoseDiff, advanced AI tools can integrate data from various imaging sources to provide a comprehensive view of the patient's anatomy and tumor characteristics. By combining information from different modalities, these tools can enhance treatment planning and decision-making in radiotherapy.